Effect of Semantic Content Generalization on Pointer Generator Network in Text Summarization

Abstract

Semantic content generalization is a method for text summarization that reduces the difficulty of training of neural networks by replacing some phrases such as named entities with generalized terms. The semantic content generalization has achieved remarkable results in enhancing the performance of the sequence to sequence attention model. Besides that, the pointer generator network could ease the training of the summarization based on a mechanism that copies words from the original text, which shares a similar idea with semantic content generalization. The purpose of this work is to test and verify the effect of semantic content generalization on the pointer generator network. Therefore, we use the preprocessing of semantic content generalization and then combine it with the pointer generator network. We examine the performance through an experiment using CNN/DailyMail dataset. From the experiment, we found that the semantic content generalization can improve the performance of the pointer generator network.